Opening Access to Southern African Research: Recommendations for University Managers
Bibliographic record
Abstract
Open access to research is no longer a fanciful notion promoted by a small group of advocates: it has become a mainstream concept embraced by governments, funders, institutions and researchers. It is an enabler of knowledge societies. UNESCO and the World Bank have endorsed the potential benefits of open access to the whole world. Open access has been shown to increase the impact of research on other sectors, notably the small business, education and health sectors. It improves effciencies in the research process wherever it is undertaken – in academia, in industry and commerce, in the cultural heritage sector and by independent researchers. Research moves more quickly and more effciently if there are no barriers to locating and accessing information. Open access also saves money and this, coupled with effciency gains, means that the future system of scholarly communication will be cheaper and better, with payoffs for producers of research and for those who can – given free access – use it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.024 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.023 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".